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• We studied the chemical and morphological characteristics of atmospheric particles. • Particles were sampled in Harbin, Northeast China, from April to June 2017. • We used spectrometers, electron microscopy, and factorial analysis to study particles. • Emission sources were mainly dust, coal/oil combustion, traffic, and industry. Detailed knowledge of aerosol chemical characteristics and emission sources play a key role in climate and human health. In this study, we collected the atmospheric aerosol particles in urban Harbin, the northernmost provincial capital in northeast China, using an ambient sampler during the period from April to June 2017. Morphological and chemical composition analyses of collected particles were performed using a Fourier transform infrared spectrometer (FTIR), an X-ray fluorescence spectrometer (XRF), and a scanning electron microscope equipped with an energy dispersion spectrum (SEM-EDS). Emission sources of these aerosol particles were determined via an enrichment factor analysis and then their variation were further discussed. During the studying period, the atmospheric particles were mainly composed of organics, inorganic ions and oxides with 20 elements (i.e., C, O, Na, Mg, Al, Si, P, and S) observed. Among the detected elements, Cr, Ni, Cu, Br, and Zn were found to be enriched, indicating considerable anthropogenic influence during high pollution days. In early April, most of the aerosol particles were emitted from coal-fired boilers and coal-fired power plants; in early May, they mainly originated from soil dust and dust, traffic, and industry emissions; and in late May and June, traffic and industry became the major aerosol sources over Harbin. [ABSTRACT FROM AUTHOR]

A combined model to simulate CO₂ and H₂O gas exchange at the leaf scale was parameterized using data obtained from in situ leaf-scale observations of diurnal and seasonal changes in CO₂ and H₂O gas exchange. The Farquhar et al.-type model of photosynthesis was parameterized by using the Bayesian approach and the Ball et al.-type stomatal conductance model was optimized using the linear least-squares procedure. The results show that the seasonal physiological changes in photosynthetic parameters (e.g., V(cmax25), J(max25), R(d25) and g(m25)) in the biochemical model of photosynthesis and m in the stomatal conductance model should be counted in estimating long-term CO₂ and H₂O gas exchange. Overall, the coupled model successfully reproduced the observed response in net assimilation and transpiration rates.